Scholarlink

<p>Autonomous docking in mobile robots requires precise control in dynamic environments that are affected by sensor noise and surface variations. Although PID controllers are widely used because of their simplicity, fixed gains often fail to adapt to environmental variability. Recent studies have explored large language models (LLMs) for dynamic PID tuning; however, their high computational overhead limits real-time deployment. To address this issue, we propose LoRA-based genetic evolution (LoGenE), a gradient-free neuroevolution framework that optimizes lightweight low-rank adaptation (LoRA) adapters for dynamic PID control. LogenE evolves lightweight adapter modules offline using control logs, eliminating the need for gradient updates or expensive real-time simulations. The resulting models are deployable on devices with minimal latency. Experiments conducted in the ROS + Gazebo simulation environment showed that LoGenE significantly improved docking performance compared to a base LLM, demonstrating robust and adaptive control suitable for real-world robotic systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Adaptive Control for Autonomous Mobility via LoGenE: Reward-guided Genetic Evolution of LoRA Adapters

  • 期刊论文

  • 发表日期：

    2025年11月4日

查看全文
  • Gihoon Song,
  • Cheolmin Jeong,
  • Chang Mook Kang

摘要

Autonomous docking in mobile robots requires precise control in dynamic environments that are affected by sensor noise and surface variations. Although PID controllers are widely used because of their simplicity, fixed gains often fail to adapt to environmental variability. Recent studies have explored large language models (LLMs) for dynamic PID tuning; however, their high computational overhead limits real-time deployment. To address this issue, we propose LoRA-based genetic evolution (LoGenE), a gradient-free neuroevolution framework that optimizes lightweight low-rank adaptation (LoRA) adapters for dynamic PID control. LogenE evolves lightweight adapter modules offline using control logs, eliminating the need for gradient updates or expensive real-time simulations. The resulting models are deployable on devices with minimal latency. Experiments conducted in the ROS + Gazebo simulation environment showed that LoGenE significantly improved docking performance compared to a base LLM, demonstrating robust and adaptive control suitable for real-world robotic systems.